 
We implement the FSL methods in PyTorch, adapting the implementation of [1]} but also borrowing from other implementations online (see individual method files in the source code for individual attribution). However, we have heavily modified these implementations to fit our imbalanced FSL framework, which also offers standard and continual FSL compatibility [2]}. We provide our implementations for ProtoMAML and BTAML for which no open-source implementation in PyTorch existed as of writing. To verify our implementations, we compare methods on the standard balanced 5-shot 5-way task with reported accuracy. Results are presented in Table REF . We see that algorithms achieve very similar performance with no less than 3% accuracy points compared to the reported performance. The discrepancies can be accounted for due to smaller training batch for SimpleShot, different augmentation strategies for the other methods, and natural variance stemming from random initialization. We show the validation performance over epochs for each method in Figure REF  on the next page.
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